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Financial transactions are an indispensable part of our day-to-day lives—individuals and families need to actively manage income and spending to ensure financial stability. Understanding spending patterns can help people make better money decisions, but reaching that understanding from the raw data is difficult for many consumers. We studied and analyzed relationships among financial transactions using a graph database, and have some interesting learnings to share about how people perceive their finances.

Pankaj Andhale is a Data Engineer at Intuit, Inc. He works on textual data enrichment platform. He uses data mining techniques and rapid experimentation to measure the success of products that will help make better data driven business decisions. He holds a Masters in Computer Science from Rochester Institute of Technology.

Lulu grew up in China. She earned her B.S degree in Maths and Physics from Tsinghua University and M.Eng degree in Operations Research from University of California, Berkeley. She joined Intuit as a technical data analyst of Financial Data Services team since June 2014. She has been working on projects related to data visualization, text mining and pattern detection. She has big passion in data mining and scalable machine learning especially in... Read More →

Neha, who hails from Ranchi, India completed her Masters in Computer Science from Northeastern University, Boston with dual specialization in the field of software engineering and networks. She is keenly interested in programming, security, data analytics and information retrieval. Ever since she joined Intuit as a Turbotax backend engineer, she has been working on engineering problems using data analytics and security. Currently, she is part of... Read More →